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Real-time video analytics typically require video frames to be processed by a query to identify objects or activities of interest while adhering to an end-to-end frame processing latency constraint. This imposes a continuous and heavy load on backend compute and network infrastructure. Video data has inherent redundancy and does not always contain an object of interest for a given query. We leverage this property of video streams to propose a lightweight Load Shedder that can be deployed on edge servers or on inexpensive edge devices co-located with cameras. The proposed Load Shedder uses pixel-level color-based features to calculate a utility score for each ingress video frame and a minimum utility threshold to select interesting frames to send for query processing. Dropping unnecessary frames enables the video analytics query in the backend to meet the end-to-end latency constraint with fewer compute and network resources. To guarantee a bounded end-to-end latency at runtime, we introduce a control loop that monitors the backend load and dynamically adjusts the utility threshold. Performance evaluations show that the proposed Load Shedder selects a large portion of frames containing each object of interest while meeting the end-to-end frame processing latency constraint. Furthermore, it does not impose a significant latency overhead when running on edge devices with modest compute resources.
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FFNet: Video Fast-Forwarding via Reinforcement Learning
For many applications with limited computation, com- munication, storage and energy resources, there is an im- perative need of computer vision methods that could select an informative subset of the input video for efficient pro- cessing at or near real time. In the literature, there are two relevant groups of approaches: generating a “trailer” for a video or fast-forwarding while watching/processing the video. The first group is supported by video summa- rization techniques, which require processing of the entire video to select an important subset for showing to users. In the second group, current fast-forwarding methods de- pend on either manual control or automatic adaptation of playback speed, which often do not present an accurate rep- resentation and may still require processing of every frame. In this paper, we introduce FastForwardNet (FFNet), a re- inforcement learning agent that gets inspiration from video summarization and does fast-forwarding differently. It is an online framework that automatically fast-forwards a video and presents a representative subset of frames to users on the fly. It does not require processing the entire video, but just the portion that is selected by the fast-forward agent, which makes the process very computationally efficient. The online nature of our proposed method also enables the users to begin fast-forwarding at any point of the video. Experiments on two real-world datasets demonstrate that our method can provide better representation of the input video (about 6%-20% improvement on coverage of impor- tant frames) with much less processing requirement (more than 80% reduction in the number of frames processed).
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- Award ID(s):
- 1724341
- PAR ID:
- 10067975
- Date Published:
- Journal Name:
- IEEE Conf. on Computer Vision and Pattern Recognition
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
